Update README.md

README.md

@@ -1,3 +1,332 @@
 ---
-license: bigscience-openrail-m
+license: bigscience-bloom-rail-1.0
+datasets:
+- tatsu-lab/alpaca
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- crayon
+- language-technologies
 ---
+
+# Bloomz 1.1B Finetuned on Instructions
+
+## Credit
+
+Code 99.99% copied from 
+
+
+*https://github.com/bofenghuang/vigogne*
+
+
+*https://colab.research.google.com/drive/1jCkpikz0J2o20FBQmYmAGdiKmJGOMo-o?usp=sharing#scrollTo=DpYr24pR8T_0*
+
+
+# Inference Code
+
+```python
+
+from peft import PeftModel
+from transformers import PreTrainedTokenizer, PreTrainedModel, AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModelForCausalLM, LoraConfig
+from typing import Optional
+from transformers import GenerationConfig
+import torch
+
+PROMPT_DICT = {
+    "prompt_input": (
+        "Below is a^n instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n"
+        "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
+    ),
+    "prompt_no_input": (
+        "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n"
+        "### Instruction:\n{instruction}\n\n### Response:\n"
+    ),
+}
+
+
+def get_model(model_name_or_path: str, load_in_8bit: bool = True, device_map="auto",
+              cpu: bool = False) -> PreTrainedModel:
+    if cpu:
+        model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map=device_map,
+                                                     low_cpu_mem_usage=True)
+    else:
+        model = AutoModelForCausalLM.from_pretrained(model_name_or_path, load_in_8bit=load_in_8bit,
+                                                     device_map=device_map, torch_dtype=torch.float16)
+
+    return model
+
+
+def get_peft_model(model: PreTrainedModel, lora_model_name_or_path: Optional[str] = None) -> PeftModelForCausalLM:
+    model = PeftModel.from_pretrained(model, lora_model_name_or_path, torch_dtype=torch.float16)
+
+    return model
+
+
+def get_tokenizer(model_name_or_path: str, max_input_len: int) -> PreTrainedTokenizer:
+    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, model_max_length=max_input_len,
+                                              padding_side="right", use_fast=False)
+
+    return tokenizer
+
+
+def get_llm_inference_model(base_model_name_or_path: str, lora_model_name_or_path: str, load_in_8bit: bool,
+                            device_map) -> PeftModel:
+    cpu = True if not torch.cuda.is_available() else False
+
+    model = get_model(base_model_name_or_path, load_in_8bit, device_map, cpu=cpu)
+
+    model = get_peft_model(model, lora_model_name_or_path=lora_model_name_or_path)
+
+    if not load_in_8bit:
+        model.half()
+
+    model.eval()
+
+    if torch.__version__ >= "2":
+        model = torch.compile(model)
+
+    return model
+
+
+def generate_prompt(example):
+    return (
+        PROMPT_DICT["prompt_input"].format_map(example)
+        if example["input"]
+        else PROMPT_DICT["prompt_no_input"].format_map(example)
+    )
+
+
+def infer(instruction: str, input_text: Optional[str] = None, temperature: float = 0.1, top_p: float = 0.95,
+          max_new_tokens: int = 512, early_stopping: bool = True, do_sample: bool = True,
+          repetition_penalty: float = 2.5) -> str:
+    prompt = generate_prompt({"instruction": instruction, "input": input_text})
+
+    tokenized_inputs = tokenizer(prompt, return_tensors="pt")
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    input_ids = tokenized_inputs["input_ids"].to(device)
+
+    generation_config = GenerationConfig(temperature=temperature, top_p=top_p, do_sample=do_sample,
+                                         repetition_penalty=repetition_penalty, early_stopping=early_stopping)
+
+    with torch.inference_mode():
+        generation_output = model.generate(input_ids=input_ids, generation_config=generation_config,
+                                           return_dict_in_generate=True, max_new_tokens=max_new_tokens)
+
+    output = generation_output.sequences[0]
+
+    output = tokenizer.decode(output, skip_special_tokens=True)
+
+    return output.split("### Response:")[1].strip()
+
+
+base_model_name_or_path = "bigscience/bloomz-1b1"
+
+lora_model_name_or_path = "crayon-coe/dolly-bloom-1b1-en"
+
+model = get_llm_inference_model(base_model_name_or_path, lora_model_name_or_path, True, "auto")
+
+tokenizer = get_tokenizer(base_model_name_or_path, 512)
+
+context = "Write a letter expressing your love for computers"
+
+output = infer(context)
+
+print(output)
+
+# Output
+# I am so grateful to have been able access this wonderful computer system and its amazing features, which I can now use daily with ease. 
+# 
+# My heartfelt thanks go out in advance of all my friends who are using it as well. 
+# Thank you again!
+
+```
+
+# Training Parameters
+
+```json
+{
+    "max_input_len": 512,
+    "load_in_8bit": True,
+    "model_name_or_path": "bigscience/bloomz-1b1",
+    "device_map": "auto",
+    "bias": "none",
+    "lora_dropout": 0.05,
+    "lora_alpha": 32,
+    "target_modules": ["query_key_value"],
+    "task_type": "CAUSAL_LM",
+    "lora_r": 16,
+    "pad_to_multiple_of": 8,
+    "num_train_epochs": 3,
+    "learning_rate": 0.0003,
+    "gradient_accumulation_steps": 16,
+    "per_device_train_batch_size": 8,
+    "val_set_size": 500,
+    "save_steps": 200,
+    "eval_steps": 200,
+    "evaluation_strategy": "steps",
+    "save_strategy": "steps"
+}
+```
+
+# Training Code
+
+```python
+# coding=utf-8
+# Code 99.99% copied and adapted from:
+#    https://github.com/bofenghuang/vigogne
+#    https://colab.research.google.com/drive/1jCkpikz0J2o20FBQmYmAGdiKmJGOMo-o?usp=sharing#scrollTo=DpYr24pR8T_0
+
+
+import os
+import sys
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Sequence
+
+import bitsandbytes as bnb
+import fire
+import torch
+import transformers
+from datasets import load_dataset
+from peft import LoraConfig, TaskType, get_peft_model, get_peft_model_state_dict, prepare_model_for_int8_training
+from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer
+
+IGNORE_INDEX = -100
+DEFAULT_PAD_TOKEN = "[PAD]"
+
+PROMPT_DICT = {
+    "prompt_input": (
+        "Below is a^n instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n"
+        "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
+    ),
+    "prompt_no_input": (
+        "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n"
+        "### Instruction:\n{instruction}\n\n### Response:\n"
+    ),
+}
+
+
+def generate_prompt(example):
+    return (
+        PROMPT_DICT["prompt_input"].format_map(example)
+        if example["input"]
+        else PROMPT_DICT["prompt_no_input"].format_map(example)
+    )
+
+
+# Modified from: https://github.com/bofenghuang/stanford_alpaca/blob/eb5b171d9b103a12a8e14e0edca9cbc45fe1d512/train.py#L166-L182
+# Almost same to transformers.DataCollatorForSeq2Seq
+@dataclass
+class DataCollatorForSupervisedDataset(object):
+    """Collate examples for supervised fine-tuning."""
+
+    tokenizer: transformers.PreTrainedTokenizer
+    pad_to_multiple_of: Optional[int] = None
+
+    def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
+        # dtype = torch.long
+        # input_ids, labels = tuple([torch.LongTensor(instance[key]) for instance in instances] for key in ("input_ids", "labels"))
+        input_ids, labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels"))
+
+        if self.pad_to_multiple_of is not None:
+            max_length_index, max_length = max(enumerate([len(input_ids_) for input_ids_ in input_ids]),
+                                               key=lambda x: x[1])
+            # int(math.ceil
+            n_padding = ((max_length // self.pad_to_multiple_of) + 1) * self.pad_to_multiple_of - max_length
+            # Pad the longest example to pad_to_multiple_of * N
+            input_ids[max_length_index].extend([self.tokenizer.pad_token_id] * n_padding)
+            labels[max_length_index].extend([IGNORE_INDEX] * n_padding)
+
+        input_ids = [torch.LongTensor(input_ids_) for input_ids_ in input_ids]
+        labels = [torch.LongTensor(labels_) for labels_ in labels]
+
+        input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=True,
+                                                    padding_value=self.tokenizer.pad_token_id)
+        labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX)
+
+        return dict(input_ids=input_ids, labels=labels, attention_mask=input_ids.ne(self.tokenizer.pad_token_id))
+
+
+def train(model_name_or_path: str, output_dir: str, data_path: str, val_set_size: int = 500,
+          model_max_length: int = 512, lora_r: int = 16, lora_alpha: int = 32, lora_dropout: float = 0.05,
+          target_modules: List[str] = ["query_key_value"], num_train_epochs: int = 3, learning_rate: float = 0.0001,
+          per_device_train_batch_size: int = 8, gradient_accumulation_steps: int = 16, **kwargs):
+    device_map = "auto"
+
+    model = AutoModelForCausalLM.from_pretrained(model_name_or_path, load_in_8bit=True, device_map=device_map)
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, model_max_length=model_max_length,
+                                              padding_side="right", use_fast=False)
+
+    model = prepare_model_for_int8_training(model)
+
+    lora_config = LoraConfig(r=lora_r, lora_alpha=lora_alpha, target_modules=target_modules, lora_dropout=lora_dropout,
+                             bias="none", task_type=TaskType.CAUSAL_LM)
+
+    model = get_peft_model(model, lora_config)
+
+    model.print_trainable_parameters()
+
+    # Load data
+    data = load_dataset("json", data_files=data_path)
+
+    def preprocess_function(example):
+        # Format prompt
+        user_prompt = generate_prompt(example)
+
+        # Get prompt length for masking
+        len_user_prompt_tokens = len(tokenizer(user_prompt, truncation=True)["input_ids"])
+
+        input_ids = tokenizer(user_prompt + example["output"] + tokenizer.eos_token, truncation=True)["input_ids"]
+        labels = [IGNORE_INDEX] * len_user_prompt_tokens + input_ids[len_user_prompt_tokens:]
+
+        return {"input_ids": input_ids, "labels": labels}
+
+    if val_set_size > 0:
+        train_val = data["train"].train_test_split(test_size=val_set_size, shuffle=True, seed=42)
+        train_data = train_val["train"].shuffle().map(preprocess_function, remove_columns=data["train"].column_names)
+        val_data = train_val["test"].map(preprocess_function, remove_columns=data["train"].column_names)
+    else:
+        train_data = data["train"].shuffle().map(preprocess_function, remove_columns=data["train"].column_names)
+        val_data = None
+
+    trainer = transformers.Trainer(
+        model=model,
+        train_dataset=train_data,
+        eval_dataset=val_data,
+        args=transformers.TrainingArguments(
+            per_device_train_batch_size=per_device_train_batch_size,
+            gradient_accumulation_steps=gradient_accumulation_steps,
+            num_train_epochs=num_train_epochs,
+            learning_rate=learning_rate,
+            fp16=True,
+            output_dir=output_dir,
+            load_best_model_at_end=True if val_set_size > 0 else False,
+            **kwargs,
+        ),
+        data_collator=DataCollatorForSupervisedDataset(tokenizer=tokenizer, pad_to_multiple_of=8),
+    )
+    print(trainer.args)
+
+    # Silence the warnings. Please re-enable for inference!
+    model.config.use_cache = False
+
+    old_state_dict = model.state_dict
+    model.state_dict = (lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())).__get__(model,
+                                                                                                          type(model))
+
+    if torch.__version__ >= "2" and sys.platform != "win32":
+        model = torch.compile(model)
+
+    trainer.train()
+
+    model.save_pretrained(output_dir)
+
+
+if __name__ == "__main__":
+    fire.Fire(train)
+
+```